Interested in this AI/ML Engineer role at Bankrate?
Apply Now →Skills & Technologies
About This Role
- *This role is open to remote or hybrid candidates (East Coast preference), with hybrid being central to our New York, NY or Charlotte area offices. Must be able to work Eastern Standard Time hours.*
We're looking for a Head of AI Search \& Organic Growth to own AI search (AEO/GEO), SEO, and content as one system — and to run it as an AI\-native operation. Bankrate's growth engine is the rate marketplace and the loyalty loop that compounds it. Organic is the demand system that feeds that engine; you own its quality, durability, and revenue contribution. You report to the CMO and partner closely with paid growth, brand, product, and analytics.
What You'll Do:
- The system (primary mandate). Own the organic system as the highest\-quality, lowest\-cost, most durable source of qualified demand into the rate marketplace — and defend the discovery surface as AI disintermediates search. You're accountable for the demand delivered into the engine and the revenue that converts from it, not traffic or rankings in the abstract.
- AI search — AEO/GEO (lead discipline). Define and drive Bankrate's strategy for visibility in AI\-generated answers and LLM citations. This is a creation mandate: build the frameworks and playbooks, stay ahead of AI Overviews, ChatGPT, Claude, and Gemini, and structure Bankrate's content and entity presence to be cited as authoritative wherever consumers ask financial questions. Partner with Brand — which owns PR, earned, and organic social — on the citation loop, since off\-site authority and social signals are direct inputs to whether Bankrate gets cited.
- SEO \& content. Maintain traditional search strength and the revenue it still drives while unifying it with AEO/GEO. Own content strategy and production so content is built to be cited and to convert — authoritative, substantiated, and defensible in a regulated category.
- Revenue \& measurement. Own revenue and contribution from organic, measured on the same LTV/CAC and incrementality basis as paid. Rankings, share of voice, and AI citation rate are leading indicators reported against revenue — not goals in themselves.
- Team \& AI\-native operations. Build and develop a high\-performing team across AEO/GEO, SEO, and content. Run the function on AI and agentic workflows — producing, optimizing, and scaling work at a pace that wasn't previously possible — and set the standard for how the team works with these tools.
What We're Looking For:
- Proven organic leadership — 4\+ years leading teams; you've built and scaled organic search functions end\-to\-end, not managed channels in isolation.
- AEO/GEO depth — genuine command of how answer engines work and how content gets cited by LLMs. You're ahead of the curve, not catching up.
- Revenue orientation — accountable for revenue or contribution from organic, not just traffic, with results to prove it.
- AI\-native operator — you build AI and agentic workflows into how your teams work, and move fast in a landscape changing under you.
- Builder mentality — energized by defining a new discipline inside an established org; you bring structure where there's ambiguity.
- Executive presence — you articulate strategy and tradeoffs clearly to the CMO and senior leadership.
Compensation:
Total Cash Compensation Range: $230,000 – $350,000 per year
*This role is eligible for a target bonus, which is included in the Total Cash Compensation Range above. This role is also eligible for Equity, which is offered separately from the Total Cash Compensation Range listed above. Actual compensation varies based on location, experience, and qualifications.*
Additionally, the following benefits are provided by Red Ventures, subject to eligibility requirements.
- Health Insurance Coverage (medical, dental, and vision)
- Life Insurance
- Short and Long\-Term Disability Insurance
- Flexible Spending Accounts
- Holiday Pay
- 401(k) with match
- Employee Assistance Program
- Paid Parental Bonding Benefit Program
- Flexible Paid Time Off (PTO): We believe time to rest and recharge is essential. That's why we offer a generous and flexible PTO policy. Full\-time employees accrue 20 days of PTO for a full calendar year annually, with an increase to 25 days after five years of service.
Who We Are:
Bankrate is where Americans go to get the best price on life's most important financial decisions. By combining proprietary data, advanced technology, and deep market coverage, Bankrate helps consumers compare options and make confident financial choices across mortgages, credit cards, savings, and more. As a rate provider to the Federal Reserve and the benchmark relied upon by major publishers and policymakers nationwide, Bankrate's rate data is the national standard. This commitment to precision is reflected across all its products. In mortgages, proprietary auction technology puts lenders in real\-time competition on price alone, consistently delivering rates in the lowest 10% in the country. For deposits, the platform features exclusively top\-tier rates in the top 10% nationally, while its credit card coverage encompasses more than 90% of the market by volume. Founded by a journalist, Bankrate remains dedicated to its founding mission of transparency, honest information, and real competition — helping to make the American dream more affordable for everyone.
Red Ventures is an equal opportunity employer that does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or any other basis protected by law. Employment at Red Ventures is based solely on a person's merit and qualifications.
We are committed to providing equal employment opportunities to qualified individuals with disabilities. This includes providing reasonable accommodation where appropriate. Should you require a reasonable accommodation to apply or participate in the job application or interview process, please contact [email protected].
If you are based in California, we encourage you to read this important information for California residents linked here.
At Red Ventures, we believe in real human connection. That's why we do not hire someone through text, social media, or email only. As part of the hiring process, you should expect live conversations with RV teammates before any offer is made. Also, keep an eye on the sender: we only use official @redventures.com email addresses at the portfolio level or business specific email addresses (e.g., @thepointsguy.com), not ones like "redventurescareer.com." We will never ask candidates to send money, buy equipment, or share financial account info during your journey with us. You can always find our open roles on redventures.com— if you receive a message that seems suspicious, please use redventures.com to verify the opportunity.
For more, the U.S. Federal Trade Commission has published helpful articles to help individuals learn more about protecting themselves from recruiter scams. If you think you've been targeted, feel free to report it to your local authorities. Stay safe out there!
\#li\-ac2
\#li\-remote
\#br
Salary Context
This $230K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bankrate, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($290K) sits 35% above the category median. Disclosed range: $230K to $350K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bankrate AI Hiring
Bankrate has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $350K - $350K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.